The most intractable and disturbing human behavior disorders involve the systems for memory and learning. The proposed work is intended to increase the basic understanding of how learning systems work, hence aid the search for ways to cure these afflictions. This proposal is to continue top-down theoretical work on basic learning dynamics. The method is to explore very simple dynamic models that account for a wide range of both steady-state and transient behavioral data. Currently non-associative processes are emphasized, as a necessary preliminary to understanding associative learning. Work is proposed in three areas: rate-sensitive effects, specifically habituation and feeding regulation; recurrent choice, a much-studied area that embraces most operant behavior; and, diffusion models for generalization and spatial orientation. Work in all three areas is based on elementary dynamic models that have already successfully simulated a substantial range of experimental results. The habituation work aims to understand the dynamics of habituation in simple systems. Are the apparent similarities between habituation in different species only at the level of the empirical phenomenon, or do they extend to dynamics? If the habituation process is similar in different species, what common properties of these different nervous systems underlie this similarity? If there are differences between the dynamic models needed to account for habituation in different species, what are the corresponding neural differences? Are habituation-like processes involved in feeding dynamics and in "higher" learning (operant and classical conditioning) and, if so, how? Is rate-sensitivity involved in recurrent choice behavior. Many experimental results suggest that it is, but no existing models take account of it. How can assignment-of-credit (response selection) be incorporated into models for choice? The work on diffusion arose from study of the dynamics of stimulus generalization, a basic property of all associative learning. A simple diffusion process developed for generalization dynamics can also solve a number of classical "spatial-insight" problems. It will be seen how far this simple idea can be extended to spatial and temporal learning. The ultimate aim is to combine these elementary processes into increasingly comprehensive, associative models whose properties can be compared to the structure and function of the neural systems that bring about learning.